Review for NeurIPS paper: Truncated Linear Regression in High Dimensions

Weaknesses: - My major concern is why the problem is difficult. Assumption 1 literally enforces that the adversary cannot pick arbitrary S, but only those such that a constant alpha-fraction of the observations are hidden/removed. Thus, suppose before removal we have a total of m samples (a, y). After removal it reduces to alpha * m pairs (a, y), which still suffices for accurate recovery provided that m O(k log n). - It is not convincing to me that the sample complexity in Theorem 3.1 is near-optimal. I know that O(k log n) is near-optimal, but does your result really imply such bound?